BrandCultivation/models/item2vec/inference.py

from database.dao.mysql_dao import MySqlDao
from models.item2vec import Item2Vec
from models.rank.data.config import OrderConfig, ProductConfig
from models.rank.data.utils import sample_data_clear
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from core import get_logger

logger = get_logger("models.item2vec")

class Item2VecModel:
    def __init__(self, city_uuid):
        self._dao = MySqlDao()
        self._city_uuid = city_uuid
        self._item2vec_model = Item2Vec(city_uuid)
        
    def generate_product_similarity_map(self, product_code):
        """根据product_code生成卷烟相似度矩阵"""
        logger.info(f"Generating similarity map for product {product_code}")
        product = self._dao.get_product_by_id(self._city_uuid, product_code)[ProductConfig.FEATURE_COLUMNS]
        product = sample_data_clear(product, ProductConfig)
        
        similarity_map = self._item2vec_model.get_similarity_map(product)
        similarity_map = pd.DataFrame(similarity_map)
        product_list = self._dao.load_product_data(self._city_uuid)[ProductConfig.FEATURE_COLUMNS + ["product_name"]]
        similarity_map = similarity_map.merge(product_list, on="product_code", how="inner")
        # self._similarity_map = self._similarity_map.query(f"product_code != {product_code}")
        return similarity_map
        
    def get_similarity_list(self, product_code, top=40):
        """获取与指卷烟最相似的top k个卷烟"""
        similarity_map = self.generate_product_similarity_map(product_code)
        similarity_list = similarity_map["product_code"].to_list()
        similarity_list = similarity_list[:top]
        return similarity_list
    
    def get_recommend_cust_list(self, product_code, top=100):
        """获取推荐的商户列表"""
        logger.info(f"Getting recommend list for product {product_code}, top={top}")
        product_list = self.get_similarity_list(product_code)
        order_data = self._dao.get_order_by_product_ids(self._city_uuid, product_list)[OrderConfig.FEATURE_COLUMNS]
        order_data["sale_qty"] = order_data["sale_qty"].fillna(0)
        order_data = order_data.groupby(["cust_code", "product_code"], as_index=False)["sale_qty"].mean()
        
        
        # 按照卷烟分组，取每款卷烟售卖最好的前50个商户
        order_data = (
            order_data
            .sort_values(["product_code", "sale_qty", "cust_code"], ascending=[True, False, True])
            .groupby("product_code")
            .head(top)
        )
        
        recommend_cust = (
            order_data.groupby(["cust_code"], as_index=False)["sale_qty"].sum()
            .query("sale_qty > 0")
            .sort_values(["sale_qty", "cust_code"], ascending=[False, True])
        )
        
        # 对销量进行归一化：先 log1p 压缩幂律分布的长尾，再 StandardScaler + sigmoid
        # 不做 log 变换时，头部商户 z-score 过大会导致 sigmoid 饱和，分数全为 100
        log_qty = np.log1p(recommend_cust["sale_qty"].values).reshape(-1, 1)
        scaler = StandardScaler()
        normalized = scaler.fit_transform(log_qty)
        recommend_cust["recommend_score"] = ((1 / (1 + np.exp(-normalized))) * 100).flatten()
        # recommend_cust = recommend_cust.rename(columns={"sale_qty": "recommend_score"})
        # recommend_cust.to_csv("./data/item2vec_recommend.csv", index=False)
        
        return recommend_cust
        
        
        
if __name__ == "__main__":
    city_uuid = "00000000000000000000000011445301"
    product_id = "350139"
    
    model = Item2VecModel(city_uuid)
    model.get_similarity_list(product_id)
    # dao = MySqlDao()
    # data = dao.get_order_by_cust_and_product(city_uuid, "445300108802", "340223")[OrderConfig.FEATURE_COLUMNS]
    # data.to_csv("./data/result.csv", index=False)